Experiences of using ECV datasets in ECMWF reanalyses including CCI applications. David Tan and colleagues ECMWF, Reading, UK

Transcription

1 Experiences of using ECV datasets in ECMWF reanalyses including CCI applications David Tan and colleagues ECMWF, Reading, UK Slide 1

2 Main points Experience shows benefit of integrated & iterative approach Ecv datasets and reanalyses benefit each other, 2 way Interaction between people who produce data, develop models and assimilate data (operational NWP, reanalysis) Reanalysis: a re processing framework that integrates diverse observations, promotes long term consistency, uncovers inconsistencies Recommendations for dataset improvement Surface winds altimeter reprocessing & inter calibration, scatterometer inter calibration & gap filling Upper air winds GOES AMV reprocessing ESA s Climate Change Initiative has recently started Slide 2

3 What is Reanalysis? A re processing framework for Slide 3 Integrating diverse observations and model information Identifying inconsistencies (obs/obs, obs/model) Based on data assimilation, but Distinct from operational numerical weather prediction (NWP) Frozen assimilation system more long term consistency Increasingly mature activity, addressing bigger challenges Variational bias correction, representation of model error Climate quality reanalyses, uncertainty characterization User interface to Obs Feedback info (open data policy)

4 Bigger challenges need a bigger team Dick Dee Paul Berrisford, Roger Brugge, Cristian Cordorean, Hans Hersbach, Carole Peubey, Paul Poli, Hitoshi Sato, David Tan Adrian Simmons, Sakari Uppala MSU Ch2 radiance bias [K], estimated by reanalysis Slide 4

5 Data assimilation for numerical weather prediction Observations Forecast model Data assimilation Dual perspective: filling in observation gaps with model info, constraining models with observations Accounting for relative error characteristics (to the extent that these are known) Slide 5

6 From weather analyses to climate reanalysis Reanalysis uses a modern forecasting/data assimilation system to reprocess (re analyse) past observations. (The observations themselves may have been re processed.) Example: Consistent representation of the Hadley circulation From ECMWF weather analyses: From reanalysis (ERA 15): Slide 6

7 Reanalysis at ECMWF towards climate quality ERA 15: ERA 40: ERA Interim: 1989 onwards ORA S3: 1959 onwards MACC: ERA CLIM: European Reanalysis of Global Climate Observations An EU FP7 project to prepare the next ECMWF reanalysis ERA 20C: 1900 onwards Slide 8

8 Observations used in ERA-Interim: Instruments Radiances from satellites Backscatter, GPSRO, AMVs from satellites GOES Reprocessing? Sondes, profilers, stations, ships, buoys, aircraft Ozone from satellites Slide 10

9 Observations used in ERA-Interim: Data counts Slide 11

10 Independent verification of MSU bias estimates Reanalysis system s VarBC adaptively estimates observation bias Recorded on-board warm target temperature changes due to orbital drift for NOAA-14 (Grody et al. 2004) Ch 2 Ch 3 Ch 4 Slide 15

11 BAMS State of the Climate Growing use of reanalysis for climate monitoring Caution is still advised! Slide 16

12 How accurate are trend estimates from reanalysis? Global mean temperatures, for MSU-equivalent vertical averages: ERA-Interim Radiosondes only (corrected) MSU only, from RSS Slide 17

13 Surface temperature anomalies for July 2010 ERA Interim Hadley Centre NASA/GISS NOAA/NCDC Slide 18

14 Larger uncertainties in precipitation trends Comparison of monthly averaged rainfall with combined rain gauge and satellite products (GPCP) Reanalysis estimates of rainfall over ocean are still problematic Results over land are much better Slide 19

15 Larger uncertainties in precipitation trends Decadal trends in precipitation, from GPCC data and from ERA-Interim: Slide 20

16 Precipitation anomalies for 1 O x1 O grid boxes Anomalies are computed relative to ( ) means for each month from ERA and GPCC respectively. Time series of 12-month running means are shown here. Slide 21

17 Access to reanalysis data at Public data server: ~6000 registered users Data products are updated monthly Full resolution data expected June 2011 Climate change monitoring tools in development Compares ECVs from reanalyses and other observational products Slide 22

18 Time series of monthly averaged products Slide 23

19 Large scale circulation indices Slide 24

20 Additional climate monitoring products in development Two dimensional time series (height/latitude/longitude) Global maps of Essential Climate Variables and climate anomalies Comparisons with other available reanalyses (JMA, NCEP, ) Comparisons with other observational products (GPCP, CCI, ) Slide 25

21 ERA data and visualisation services We will generate 2 Pb data products by 2017 (ERA Interim: 50 Tb) We expect a large number of users for these products ERA 40 public data server had registered users ERA Interim data server has ~6000 registered users adding 300 per month We will provide web access to full resolution reanalysis data ECMWF is no longer required to apply an information charge Cost of data services is substantial (but not yet funded) We will provide web access to observation feedback Analysis and background departures; error estimates for observations We will provide web access to data visualisation tools Includes climate monitoring facilities Need separate funding to do this right Slide 26

22 Long Term Assessment of ERS 1 & ERS 2 Wind and Wave Products Using ERA Interim Saleh Abdalla, Hans Hersbach and Jean Raymond Bidlot ECMWF, Reading, UK Slide 27

23 Altimeter winds need inter calibration & consistent reprocessing A&B C D,E,F G ERS-2 Global NH Tropics SH Bias [=Alt-Mod] (m/s) ERS Wind Speed SDD (m/s) Slide 34

24 Scatterometer winds need inter calibration + gap filling ERS-1 QuikSCAT ERS-2 (global) ERS-2 (limited coverage) Slide 37

25 ESA s Climate Change Initiative (CCI) More from Roger Saunders in next talk The CCI aims to produce climate quality datasets Mainly from reprocessing of existing satellite data Initially funded for 6 years Phase 1, : R&D, prototyping Phase 2, : Implement operational systems Sub projects responsible for delivering specific ECV products Encouraged to interact and address consistency Cross cutting sub project Climate Modelling Users Group Advise on relevance to the user community, +++ Slide 45

26 ESA CCI starts with 11 GCOS ECVs Atmosphere Ocean Terrestrial ( 3, 7, 4 ) Air Temperature; Precipitation ; Air pressure; Surface Water vapour; Surface radiation budget; ( 0, 0, 6 ) Wind Speed & direction; Cloud properties, Upper air Wind speed & direction ( 1, 1, 3 ) Earth radiation budget; Upper-air temperature; Water vapour; Composition Carbon dioxide Methane & other GHGs; ( 3, 0, 0 ) Ozone; Aerosol properties Sea-surface Temp; Sea-level; Sea-ice; Ocean colour; Surface Sea state; Sea-surface salinity ( 4, 2, 1 ) Carbon dioxide partial pressure Sub-surface Temperature; Salinity; Current; Nutrients; Carbon; ( 0, 0, 7 ) Ocean tracers; Phytoplankton Glaciers & ice caps; Land Cover; Fire disturbance Fraction of absorbed photo-synthetically active radiation; LAI, Albedo Biomass, Lake levels, Snow cover, Soil moisture Water use, Ground water, River discharge Permafrost and seasonally-frozen ground CCI First Steps (11 ECVs) : Later in CCI (10 ECVs) : Not in CCI (24 ECVs) Slide 46

27 Main points (again) Experience shows benefit of integrated & iterative approach Ecv datasets and reanalyses benefit each other, 2 way Interaction between people who produce data, develop models and assimilate data (operational NWP, reanalysis) Reanalysis: a re processing framework that integrates diverse observations, promotes long term consistency, uncovers inconsistencies Recommendations for dataset improvement Surface winds altimeter reprocessing & inter calibration, scatterometer inter calibration & gap filling Upper air winds GOES AMV reprocessing ESA s Climate Change Initiative has recently started Slide 49

28 Summary and conclusions Various roles for reanalysis in ECV activities: Source of input data for ECV retrievals Source of alternative ECV estimates Tools for confronting models with observations Assessments of ECV products, singly and combined/integrated Assessments of input observations used in ECV production Assimilation of input observations? Reanalysis provides a unifying framework for integrating climate information from many sources Progress requires sustained long-term research and development Expansion of web services for data and visualisation requires some additional resources Slide 50 Better observations Better models Better reanalysis